1 Following the best practice of responsible data acquisition, handling, classification, and management must be a priority to ensure that results and outcomes align with the AI system’s set goals and objectives.

2 AI System Owners should be accountable for the design and implementation of AI systems in such a way as to ensure that personal information is protected throughout the life cycle of the AI system.

1 To ensure that AI models embody a human centric build and design that requires adhering to practices of responsible and ethical data management frameworks and processes to be followed according to best practices and data regulations within KSA.

2 When planning and designing AI systems, due consideration should be given to preventing and helping address social and environmental issues in a way that will ensure sustainable social and ecological responsibility.

5 All critical decision points in the system design should be subject to sign off by relevant stakeholders to minimize risks and make stakeholders accountable for the decisions.

Principle 7 – accountability & Responsibility

Principle 7 – Accountability & responsibility

The accountability and responsibility principle holds designers, vendors, procurers, developers, owners and assessors of AI systems and the technology itself ethically responsible and liable for the decisions and actions that may result in potential risk and negative effects on individuals and communities.

The accountability and responsibility principle holds designers, vendors, procurers, developers, owners and assessors of AI systems and the technology itself ethically responsible and liable for the decisions and actions that may result in potential risk and negative effects on individuals and communities.

The accountability and responsibility principle holds designers, vendors, procurers, developers, owners and assessors of AI systems and the technology itself ethically responsible and liable for the decisions and actions that may result in potential risk and negative effects on individuals and communities.

The designers, developers, and people who implement the AI system should be identifiable and assume responsibility and accountability for any potential damage the technology has on individuals or communities, even if the adverse impact is unintended.

The designers, developers, and people who implement the AI system should be identifiable and assume responsibility and accountability for any potential damage the technology has on individuals or communities, even if the adverse impact is unintended.

The accountability and responsibility principle is closely related to the fairness principle.

The accountability and responsibility principle is closely related to the fairness principle.

The parties responsible for the AI system should ensure that the fairness of the system is maintained and sustained through control mechanisms.

1 This step is crucial to design or procure an AI System in an accountable and responsible manner.

1 This step is crucial to design or procure an AI System in an accountable and responsible manner.

The ethical responsibility and liability for the outcomes of the AI system should be attributable to stakeholders who are responsible for certain actions in the AI System Lifecycle.

The ethical responsibility and liability for the outcomes of the AI system should be attributable to stakeholders who are responsible for certain actions in the AI System Lifecycle.

It is essential to set a robust governance structure that defines the authorization and responsibility areas of the internal and external stakeholders without leaving any areas of uncertainty to achieve this principle.

1 An important aspect of the accountability and Responsibility principle during Prepare Input Data step in the AI System Lifecycle is data quality as it affects the outcome of the AI model and decisions accordingly.

1 An important aspect of the Accountability and responsibility principle during Prepare Input Data step in the AI System Lifecycle is data quality as it affects the outcome of the AI model and decisions accordingly.

The data preparation process and data quality checks should be documented and validated by responsible parties.

To achieve this, the technical stakeholders who build and validate models should be responsible for these decisions.

2 Assigning the appropriate ownership and communicating responsibilities will set the tone for accountability that would aid in steering the development of the AI system on good reasons, solid interference, and will allow the intervention of human critical judgement and expertise.

1 The responsibility and associated liability in the Deploy and Monitor step should be set clearly.